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DA-Net: A Double Alignment Multimodal Learning Network for Point Cloud Quality Assessment.

Xinqiang Wu, Zhouyan He, Ting Luo

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 9, 2025

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This study introduces a novel network for point cloud quality assessment (PCQA) that aligns 3D and 2D data for better distortion representation. The proposed method improves performance and reduces complexity in multimodal PCQA.

Area of Science:

Computer Vision
Multimedia Signal Processing

Background:

Existing point cloud quality assessment (PCQA) methods struggle with spatial correspondence and modality heterogeneity.
Current approaches often rely on complex fusion mechanisms, leading to limited performance and high computational costs.

Purpose of the Study:

To develop a novel multimodal learning network for enhanced point cloud quality assessment.
To address the limitations of existing methods by introducing effective alignment strategies.

Main Methods:

A Double Alignment Multimodal Learning Network (DA-Net) was proposed.
Key strategies include spatial pre-alignment using an adaptive patch projection module (APPM) and uniform feature alignment via feature disentanglement (FDM) and mapping (FMM) modules.
Simple integration of multimodal features for quality score regression.

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Main Results:

DA-Net demonstrated outstanding performance and generalization capabilities in PCQA.
The proposed method achieved lower computational complexity compared to existing multimodal PCQA techniques.
Experimental results validate the effectiveness of the alignment strategies.

Conclusions:

DA-Net effectively overcomes the challenges of spatial correspondence and modality heterogeneity in multimodal PCQA.
The network offers a more efficient and performant solution for assessing point cloud quality.
The proposed alignment strategies provide a promising direction for future research in this field.